system

Abstract

The system according to the embodiment aims to enable consumers to easily determine the eco-level of a product and make environmentally friendly choices. [Solution] A system according to an embodiment includes a camera unit, an image recognition unit, a determination unit, and a display unit. The camera unit photographs a product. The image recognition unit analyzes the image photographed by the camera unit. The determination unit determines an eco level based on the product identified by the image recognition unit. The display unit displays the eco level determined by the determination unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology makes it difficult to easily determine the environmental impact of a product, making it difficult for consumers to make environmentally friendly choices.

[0005] The system according to the embodiment aims to enable consumers to easily determine the eco-level of a product and make environmentally friendly choices. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an image recognition unit, a determination unit, and a display unit. The camera unit photographs a product. The image recognition unit analyzes the image photographed by the camera unit. The determination unit determines an eco level based on the product identified by the image recognition unit. The display unit displays the eco level determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment allows consumers to easily determine the eco-level of a product and make environmentally friendly choices. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An eco-analysis tool according to an embodiment of the present invention is a system that photographs a product, determines its eco-level, and displays it. The eco-analysis tool instantly determines the eco-level of a product by photographing the product with a camera using an application installed on a smartphone or tablet. This application is equipped with image recognition technology, which can recognize words written on the product and identify and analyze the product. For example, in the eco-analysis tool, a user photographs a product using the camera on their smartphone or tablet. For example, the eco-analysis tool sends the image captured with the camera to the image recognition technology. The image recognition technology recognizes words written on the product from the image and identifies the product. For example, it can read information such as the product name, ingredient list, and manufacturer. Next, the eco-analysis tool analyzes the product's eco-level based on the product identified using the image recognition technology. The eco-level determination takes into account information such as the product's manufacturing process, materials used, and recyclability. For example, for plastic products, the carbon dioxide emissions during the manufacturing process and the ease of recycling are evaluated. Next, the eco-analysis tool instantly displays the analysis results on the user's smartphone or tablet. This allows the user to select environmentally friendly products based on the displayed eco-level. For example, choosing products with a high eco-level can reduce environmental impact. This makes it easy for everyone to take environmentally friendly actions. For example, because users can instantly check the eco-level when selecting a product, it becomes easier to make environmentally conscious choices in their daily lives. Furthermore, the quick determination of eco-levels eliminates the need for additional shopping effort. Furthermore, the use of image recognition technology enables highly accurate product identification and analysis. This allows users to obtain reliable eco-level information. For example, even if the same product has different manufacturers or ingredients, the differences can be accurately recognized and the eco-level determined. The eco-analysis tool can be used simply by installing it on a smartphone or tablet; no special equipment or settings are required. This makes it easy for a wide range of users to use. Furthermore, application updates ensure that the latest eco-level information is always available.This makes it easy for everyone to take environmentally friendly actions with the Eco Analysis Tool. For example, when selecting a product, users can instantly check the eco level, making it easier to make environmentally conscious choices in their daily lives. Furthermore, the quick determination of eco levels eliminates the need for additional shopping effort. Furthermore, the use of image recognition technology enables highly accurate product identification and analysis. This allows users to obtain highly reliable eco level information. For example, even if the same product has different manufacturers or ingredients, the differences can be accurately recognized and the eco level determined. The Eco Analysis Tool can be used simply by installing it on a smartphone or tablet; no special equipment or settings are required. This makes it easy for a wide range of users to use. Furthermore, by updating the application, the latest eco level information can always be provided.

[0029] The eco-analysis tool according to the embodiment includes a camera unit, an image recognition unit, a determination unit, and a display unit. The camera unit photographs a product. For example, the camera unit photographs the product using a smartphone or tablet camera. The camera unit can also automatically adjust the angle and lighting of the product during photography to obtain the optimal image. For example, the camera unit can automatically adjust the angle of the product to photograph it in the most visible position. The camera unit can also detect the amount of ambient light and photograph it at the optimal brightness. The camera unit can also adjust the direction of the light to avoid reflections on the product during photography. The image recognition unit analyzes the image photographed by the camera unit. For example, the image recognition unit recognizes words written on the product from the image and identifies the product. The image recognition unit can read information such as the product name, ingredient list, and manufacturer. The image recognition unit can also analyze the label or package design of the product to identify the product. For example, the image recognition unit can analyze the text written on the product label to identify the product. The image recognition unit can also analyze the package design to identify the product. Furthermore, the image recognition unit can read and identify the barcode or two-dimensional code (e.g., QR Code (registered trademark)) of the product. For example, the image recognition unit can read the barcode of the product to identify the product. The image recognition unit can also read the two-dimensional code of the product to identify the product. The determination unit determines the eco level based on the product identified by the image recognition unit. For example, the determination unit determines the eco level based on information about the product's manufacturing process, the materials used, and recyclability. The determination unit can determine the eco level by taking into account, for example, the energy consumption and carbon dioxide emissions in the product's manufacturing process. The determination unit can also determine the eco level by evaluating the type of materials used in the product and its recyclability. Furthermore, the determination unit can determine the eco level by taking into account the product's disposal method and the difficulty of recycling. For example, the determination unit can analyze the product's disposal method, evaluate the product's recyclability, and determine the eco level. The display unit displays the eco level determined by the determination unit.For example, the display unit instantly displays the determined eco level on the user's smartphone or tablet. The display unit can, for example, graphically display detailed information about the eco level. The display unit can also display a chronological history of the eco level. Furthermore, the display unit can display comparative information about the eco levels. For example, the display unit can compare multiple products and display their eco levels. As a result, the eco analysis tool according to the embodiment allows the user to select environmentally friendly products by photographing the products and determining and displaying their eco levels.

[0030] The eco-analysis tool includes a standard setting unit that sets eco-level determination criteria. The standard setting unit sets the eco-level determination criteria. For example, the standard setting unit can set the eco-level determination criteria based on information such as the product's manufacturing process, the materials used, and recyclability. The standard setting unit can also set the eco-level determination criteria based on a user's individual environmental awareness and values. For example, the standard setting unit can analyze the user's environmental awareness and reflect it in the eco-level determination criteria. The standard setting unit can also set the eco-level determination criteria taking the user's values ​​into consideration. Furthermore, the standard setting unit can set different eco-level determination criteria for each product category. For example, the standard setting unit can set different eco-level determination criteria for each product category (food, clothing, electrical appliances, etc.). In this way, setting the eco-level determination criteria improves the accuracy of the determination.

[0031] The eco-analysis tool includes an update unit that updates data. The update unit updates the data. For example, the update unit can automatically acquire the latest environmental data and regulatory information and reflect it in the eco-level determination. The update unit can also update the data by reflecting user feedback. For example, the update unit can analyze user feedback and update the eco-level determination data. The update unit can also integrate information from different data sources to expand the data. For example, the update unit can acquire environmental data from multiple data sources and reflect it in the eco-level determination. In this way, by updating the data, it is possible to always provide the latest eco-level information.

[0032] The image recognition unit can recognize words written on a product and identify the product. The image recognition unit can, for example, recognize words written on a product and identify the product. For example, the image recognition unit can read information such as the product name, ingredient list, and manufacturer. The image recognition unit can also analyze the product's label or package design to identify the product. For example, the image recognition unit can analyze the characters written on the product's label to identify the product. The image recognition unit can also analyze the product's package design to identify the product. The image recognition unit can also read the product's barcode or two-dimensional code to identify the product. For example, the image recognition unit can read the product's barcode to identify the product. The image recognition unit can also read the product's two-dimensional code to identify the product. In this way, the product can be accurately identified by recognizing the words written on the product.

[0033] The determination unit can determine the eco level based on information about the product's manufacturing process, the materials used, and recyclability. The determination unit can determine the eco level based on information about the product's manufacturing process, the materials used, and recyclability. For example, the determination unit can determine the eco level by taking into account the amount of energy consumed and carbon dioxide emissions in the product's manufacturing process. The determination unit can also determine the eco level by evaluating the types of materials used in the product and their recyclability. Furthermore, the determination unit can determine the eco level by taking into account the product's disposal method and the difficulty of recycling. For example, the determination unit can analyze the product's disposal method, evaluate the recyclability, and determine the eco level. In this way, the eco level can be determined with high accuracy by taking into account the product's manufacturing process, materials, and recyclability.

[0034] The display unit can instantly display the determined eco level on the user's smartphone or tablet. For example, the display unit can instantly display the determined eco level on the user's smartphone or tablet. For example, the display unit can graphically display detailed information about the eco level. The display unit can also display the eco level history in chronological order. Furthermore, the display unit can display comparative information about the eco levels. For example, the display unit can compare the eco levels of multiple products and display them. This allows the user to quickly select environmentally friendly products by instantly displaying the eco level.

[0035] The camera unit can automatically adjust the angle and lighting of the product when taking a photo to obtain the optimal image. The camera unit can automatically adjust the angle and lighting of the product to obtain the optimal image. For example, the camera unit can automatically adjust the angle of the product to take a photo at the most visible position. The camera unit can also detect the amount of ambient light and take a photo at the optimal brightness. Furthermore, the camera unit can adjust the direction of the light to avoid reflections on the product when taking a photo. In this way, the optimal image can be obtained by automatically adjusting the angle and lighting of the product. Some or all of the above-mentioned processing in the camera unit may be performed using, or without, AI, for example. For example, the camera unit can input image data of the product to a generation AI and cause the generation AI to obtain the optimal image.

[0036] The camera unit can continuously capture multiple images during shooting and select the clearest image. For example, the camera unit can continuously capture multiple images during shooting and select the clearest image. For example, the camera unit can continuously capture five images and select the clearest image from among them. The camera unit can also analyze the continuously captured images and select the image with the least blur. Furthermore, the camera unit can select the image with the most accurate color from the continuously captured images. In this way, by continuously capturing multiple images, the clearest image can be selected. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input continuously captured image data to a generation AI and have the generation AI select the clearest image.

[0037] The camera unit may be equipped with a filtering function that removes background other than the product when capturing an image. The camera unit may be equipped with a filtering function that removes background other than the product when capturing an image. For example, the camera unit may detect background other than the product and automatically remove it. The camera unit may also perform filtering that blurs the background to highlight the product. Furthermore, the camera unit may also trim unnecessary parts other than the product to obtain an optimal image. In this way, an image that highlights the product can be obtained by automatically removing background other than the product. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit may input captured image data to a generation AI and have the generation AI remove the background.

[0038] The camera unit can prioritize photographing highly relevant products by taking into account the user's geographical location information when taking a photo. For example, the camera unit can prioritize photographing highly relevant products by taking into account the user's geographical location information when taking a photo. For example, the camera unit can detect the user's current location and prioritize photographing products that are popular in the area. The camera unit can also prioritize photographing local specialties based on the user's location information. Furthermore, the camera unit can prioritize photographing products sold in nearby stores by taking into account the user's location information. In this way, highly relevant products can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the camera unit may be performed using, or without, AI. For example, the camera unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant products.

[0039] The camera unit can analyze the user's past shooting history and suggest the optimal shooting method when taking a photo. For example, the camera unit can analyze the user's past shooting history and suggest the optimal shooting method when taking a photo. For example, the camera unit can analyze the user's past shooting history and suggest the most frequently used shooting method. The camera unit can also suggest the optimal shooting angle and lighting conditions based on the user's past shooting history. Furthermore, the camera unit can suggest optimal filtering settings by referring to the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing the user's past shooting history. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's past shooting history data into a generation AI and have the generation AI suggest the optimal shooting method.

[0040] The camera unit can analyze the user's social media activity and capture related products when taking a photo. For example, the camera unit can analyze the user's social media activity and capture related products when taking a photo. For example, the camera unit can analyze the user's social media posts and capture related products. The camera unit can also capture related products based on the user's social media check-in information. Furthermore, the camera unit can also capture related products by referring to the activity of the user's friends on social media. In this way, related products can be captured by analyzing the user's social media activity. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's social media data into a generation AI and cause the generation AI to select related products.

[0041] The image recognition unit can analyze and identify the product's label or package design during recognition. The image recognition unit, for example, analyzes and identifies the product's label or package design during recognition. For example, the image recognition unit can analyze the characters written on the product's label to identify the product. The image recognition unit can also analyze the product's package design to identify the product. Furthermore, the image recognition unit can analyze the product's label and package design in combination to identify the product. In this way, the product can be accurately identified by analyzing the product's label or package design. Some or all of the above-mentioned processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input data on the product's label or package design into a generation AI and have the generation AI identify the product.

[0042] The image recognition unit can read and identify the barcode or two-dimensional code of a product during recognition. The image recognition unit, for example, reads and identifies the barcode or two-dimensional code of a product during recognition. For example, the image recognition unit can read the barcode of a product to identify the product. The image recognition unit can also read the two-dimensional code of a product to identify the product. Furthermore, the image recognition unit can read both the barcode and the two-dimensional code to identify the product. This makes it possible to accurately identify the product by reading the barcode or two-dimensional code of the product. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input data of the barcode or two-dimensional code of the product to the generation AI and have the generation AI identify the product.

[0043] The image recognition unit can analyze and identify the shape and color of a product during recognition. The image recognition unit, for example, analyzes and identifies the shape and color of a product during recognition. For example, the image recognition unit can analyze the shape of a product to identify the product. The image recognition unit can also analyze the color of a product to identify the product. Furthermore, the image recognition unit can analyze a combination of the shape and color of a product to identify the product. In this way, by analyzing the shape and color of a product, the product can be accurately identified. Some or all of the above-mentioned processing in the image recognition unit may be performed using, or without, AI, for example. For example, the image recognition unit can input data on the shape and color of a product to a generation AI and cause the generation AI to identify the product.

[0044] The image recognition unit can prioritize recognizing highly relevant products by taking into account the user's geographical location information during recognition. For example, the image recognition unit can prioritize recognizing highly relevant products by taking into account the user's geographical location information during recognition. For example, the image recognition unit can detect the user's current location and prioritize recognizing products that are popular in that area. The image recognition unit can also prioritize recognizing local specialties based on the user's location information. Furthermore, the image recognition unit can prioritize recognizing products sold in nearby stores by taking into account the user's location information. In this way, highly relevant products can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the image recognition unit may be performed using, or without, AI. For example, the image recognition unit can input the user's location information data into the generation AI and cause the generation AI to recognize highly relevant products.

[0045] The image recognition unit can analyze the user's past recognition history during recognition and suggest the optimal recognition method. For example, the image recognition unit can analyze the user's past recognition history and suggest the most frequently used recognition method during recognition. The image recognition unit can also suggest the optimal recognition angle and lighting conditions based on the user's past recognition history. Furthermore, the image recognition unit can also suggest optimal filtering settings by referring to the user's past recognition history. In this way, the optimal recognition method can be suggested by analyzing the user's past recognition history. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input the user's past recognition history data into a generation AI and have the generation AI suggest the optimal recognition method.

[0046] The image recognition unit can analyze the user's social media activity during recognition and recognize related products. For example, the image recognition unit can analyze the user's social media activity during recognition and recognize related products. For example, the image recognition unit can analyze the user's social media posts and recognize related products. The image recognition unit can also recognize related products based on the user's social media check-in information. Furthermore, the image recognition unit can recognize related products by referring to the activity of the user's friends on social media. In this way, related products can be recognized by analyzing the user's social media activity. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input the user's social media data into a generation AI and cause the generation AI to recognize related products.

[0047] The determination unit can take into account the energy consumption in the manufacturing process of the product when making the determination. The determination unit, for example, takes into account the energy consumption in the manufacturing process of the product when making the determination. For example, the determination unit can analyze the energy consumption used in the manufacturing process of the product and determine the eco level. The determination unit can also determine the eco level by taking into account the carbon dioxide emissions in the manufacturing process of the product. Furthermore, the determination unit can evaluate the energy efficiency in the manufacturing process of the product and determine the eco level. In this way, by taking into account the energy consumption in the manufacturing process of the product, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input product manufacturing process data into the generation AI and cause the generation AI to analyze the energy consumption.

[0048] The determination unit can evaluate the disposal method and recyclability of the product after use at the time of determination. The determination unit, for example, evaluates the disposal method and recyclability of the product after use at the time of determination. For example, the determination unit can analyze the disposal method of the product, evaluate the recyclability, and determine the eco level. The determination unit can also determine the eco level taking into account the difficulty of recycling the product. Furthermore, the determination unit can evaluate the environmental impact of the product after disposal and determine the eco level. In this way, by evaluating the disposal method and recyclability of the product after use, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the disposal method and recyclability of the product into the generation AI and have the generation AI perform the evaluation.

[0049] The determination unit can determine the eco level by taking into account the transport distance and means of transport of the product during the determination. For example, the determination unit can determine the eco level by taking into account the transport distance and means of transport of the product during the determination. For example, the determination unit can analyze the transport distance of the product and determine the eco level. The determination unit can also determine the eco level by taking into account the means of transport of the product (ship, airplane, truck, etc.). Furthermore, the determination unit can evaluate the energy consumption during the transport process of the product and determine the eco level. In this way, by taking into account the transport distance and means of transport of the product, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the transport distance and means of transport of the product into the generation AI and cause the generation AI to perform the evaluation.

[0050] The determination unit can apply regional eco-level standards by taking into account the user's geographical location information during the determination. For example, the determination unit can apply regional eco-level standards by taking into account the user's geographical location information during the determination. For example, the determination unit can detect the user's current location and apply the regional eco-level standards. The determination unit can also determine the eco level by taking into account regional environmental regulations based on the user's location information. Furthermore, the determination unit can evaluate regional recyclability by taking into account the user's location information. In this way, regional eco-level standards can be applied by taking into account the user's geographical location information. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's location information data into the generation AI and cause the generation AI to apply regional eco-level standards.

[0051] The judgment unit can analyze the user's past judgment history and propose an optimal judgment method when making a judgment. For example, the judgment unit can analyze the user's past judgment history and propose an optimal judgment method when making a judgment. For example, the judgment unit can analyze the user's past judgment history and propose the most frequently used judgment method. The judgment unit can also propose optimal judgment criteria based on the user's past judgment history. Furthermore, the judgment unit can also propose an optimal eco level display method by referring to the user's past judgment history. In this way, the optimal judgment method can be proposed by analyzing the user's past judgment history. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past judgment history data into a generation AI and cause the generation AI to propose an optimal judgment method.

[0052] The determination unit can analyze the user's social media activity at the time of determination and determine the eco-level of the related products. For example, the determination unit can analyze the user's social media activity at the time of determination and determine the eco-level of the related products. For example, the determination unit can analyze the content of the user's social media posts and determine the eco-level of the related products. The determination unit can also determine the eco-level of the related products based on the user's check-in information on social media. Furthermore, the determination unit can determine the eco-level of the related products by referring to the activity of the user's friends on social media. In this way, the eco-level of the related products can be determined by analyzing the user's social media activity. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media data into a generation AI and cause the generation AI to determine the eco-level of the related products.

[0053] The display unit can graphically display the detailed eco level information when displaying the data. The display unit, for example, graphically displays the detailed eco level information when displaying the data. For example, the display unit can display the detailed eco level information in a graph or chart. The display unit can also visually display the detailed eco level information using icons or symbols. Furthermore, the display unit can display the detailed eco level information in an infographic. This makes it easier for the user to visually understand the detailed eco level information by graphically displaying it. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the detailed eco level data to a generation AI and cause the generation AI to execute a graphical display.

[0054] The display unit can display the eco level history in chronological order when displaying. The display unit, for example, displays the eco level history in chronological order when displaying. For example, the display unit can display the eco level history in a chronological graph. The display unit can also display the eco level history in a chronological list. Furthermore, the display unit can display the eco level history in chronological order in a calendar format. By displaying the eco level history in chronological order, the user can understand past fluctuations in the eco level. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input eco level history data to a generation AI and cause the generation AI to display the data in chronological order.

[0055] The display unit can display comparative information on eco levels when displaying. The display unit, for example, displays comparative information on eco levels when displaying. For example, the display unit can compare multiple products and display their eco levels. The display unit can also compare different batches of the same product and display their eco levels. Furthermore, the display unit can compare products with high and low eco levels and display them. This allows the user to compare and select multiple products by displaying comparative information on eco levels. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input comparative information data on eco levels to a generation AI and cause the generation AI to display the comparative information.

[0056] The display unit can display the eco level for each region taking into account the user's geographical location information when displaying the data. The display unit, for example, can display the eco level for each region taking into account the user's geographical location information when displaying the data. For example, the display unit can detect the user's current location and display the eco level for that region. The display unit can also display the eco level for each region based on the user's location information. Furthermore, the display unit can display the recyclability for each region taking into account the user's location information. In this way, the eco level for each region can be displayed by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the user's location information data to a generation AI and cause the generation AI to display the eco level for each region.

[0057] The display unit can analyze the user's past display history and propose an optimal display method when displaying. For example, the display unit can analyze the user's past display history and propose an optimal display method when displaying. For example, the display unit can analyze the user's past display history and propose the most frequently used display method. The display unit can also propose an optimal display order based on the user's past display history. Furthermore, the display unit can also propose an optimal eco level display method by referring to the user's past display history. In this way, the optimal display method can be proposed by analyzing the user's past display history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display history data to a generation AI and cause the generation AI to propose an optimal display method.

[0058] The display unit can analyze the user's social media activity and display the associated eco level when displaying the data. For example, the display unit can analyze the user's social media activity and display the associated eco level when displaying the data. For example, the display unit can analyze the content of the user's social media posts and display the associated eco level. The display unit can also display the associated eco level based on the user's social media check-in information. Furthermore, the display unit can display the associated eco level with reference to the activity of the user's friends on social media. In this way, the associated eco level can be displayed by analyzing the user's social media activity. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and cause the generation AI to display the associated eco level.

[0059] The standard setting unit can reflect the latest environmental regulations and standards when setting the standards. The standard setting unit, for example, reflects the latest environmental regulations and standards when setting the standards. For example, the standard setting unit can automatically obtain the latest environmental regulations and reflect them in the eco-level standards. The standard setting unit can also analyze the latest environmental standards and reflect them in the eco-level standards. Furthermore, the standard setting unit can update the eco-level standards based on the latest environmental data. In this way, by reflecting the latest environmental regulations and standards, the eco-level can always be determined based on the latest standards. Some or all of the above-mentioned processing in the standard setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the standard setting unit can input data on the latest environmental regulations and standards into the generation AI and cause the generation AI to update the standards.

[0060] The standard setting unit can reflect the user's individual environmental awareness and values ​​when setting the standard. For example, the standard setting unit can analyze the user's environmental awareness and reflect it in the eco-level standard. The standard setting unit can also set the eco-level standard taking the user's values ​​into consideration. Furthermore, the standard setting unit can set the eco-level standard by combining the user's environmental awareness and values. This allows the user's individual environmental awareness and values ​​to be reflected, thereby enabling the optimal standard to be set for the user. Some or all of the above-mentioned processing in the standard setting unit can be performed using, or without, AI. For example, the standard setting unit can input data on the user's environmental awareness and values ​​into the generation AI and have the generation AI set the standard.

[0061] The standard setting unit can apply different standards to each product category when setting the standards. For example, the standard setting unit can set different eco-level standards for each product category (food, clothing, electrical appliances, etc.). The standard setting unit can also analyze the environmental impact of each product category and set eco-level standards. Furthermore, the standard setting unit can set eco-level standards taking into account the recyclability of each product category. This allows for more accurate determination of eco-levels by applying different standards to each product category. Some or all of the above-mentioned processing in the standard setting unit can be performed using, for example, AI, or can be performed without using AI. For example, the standard setting unit can input data for each product category into a generation AI and have the generation AI set the standards.

[0062] The standard setting unit can apply regional standards by taking into account the user's geographical location information when setting the standards. For example, the standard setting unit can apply regional standards by taking into account the user's geographical location information when setting the standards. For example, the standard setting unit can detect the user's current location and apply the eco-level standard for that region. The standard setting unit can also set eco-level standards by taking into account regional environmental regulations based on the user's location information. Furthermore, the standard setting unit can evaluate the recyclability of each region by taking into account the user's location information. In this way, regional standards can be applied by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the standard setting unit can be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's location information data into the generation AI and cause the generation AI to apply regional standards.

[0063] The standard setting unit can analyze the user's past standard setting history and propose optimal standards when setting standards. For example, the standard setting unit can analyze the user's past standard setting history and propose optimal standards when setting standards. For example, the standard setting unit can analyze the user's past standard setting history and propose the most frequently used standard. The standard setting unit can also propose optimal standards based on the user's past standard setting history. Furthermore, the standard setting unit can propose optimal eco level standards by referring to the user's past standard setting history. In this way, optimal standards can be proposed by analyzing the user's past standard setting history. Some or all of the above-mentioned processing in the standard setting unit can be performed, for example, using AI or without AI. For example, the standard setting unit can input the user's past standard setting history data into a generation AI and cause the generation AI to propose optimal standards.

[0064] The standard setting unit may analyze the user's social media activity and set the relevant standard when setting the standard. For example, the standard setting unit may analyze the user's social media activity and set the relevant standard when setting the standard. For example, the standard setting unit may analyze the user's social media posts and set the relevant eco-level standard. The standard setting unit may also set the relevant eco-level standard based on the user's social media check-in information. Furthermore, the standard setting unit may set the relevant eco-level standard with reference to the activities of the user's friends on social media. In this way, the relevant standard can be set by analyzing the user's social media activity. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the standard setting unit may input the user's social media data into a generation AI and cause the generation AI to set the relevant standard.

[0065] The update unit can automatically acquire the latest environmental data and regulatory information during an update. The update unit automatically acquires the latest environmental data and regulatory information during an update, for example. For example, the update unit can automatically acquire the latest environmental data and reflect it in determining the eco level. The update unit can also automatically acquire the latest environmental regulatory information and reflect it in the eco level criteria. Furthermore, the update unit can acquire a combination of the latest environmental data and regulatory information and reflect it in determining the eco level. In this way, by automatically acquiring the latest environmental data and regulatory information, the eco level can always be determined using the latest information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input the latest environmental data and regulatory information data into the generation AI and cause the generation AI to acquire the data.

[0066] The update unit can update the data by reflecting user feedback during an update. The update unit, for example, updates the data by reflecting user feedback during an update. For example, the update unit can analyze user feedback and update the eco level determination data. The update unit can also adjust the eco level criteria based on user feedback. Furthermore, the update unit can update the eco level display method by reflecting user feedback. This enables data updating that is more suitable for the user by reflecting user feedback. Some or all of the above-described processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input user feedback data to a generation AI and cause the generation AI to update the data.

[0067] The update unit can integrate information from different data sources to enrich the data during an update. For example, the update unit can integrate information from different data sources to enrich the data during an update. For example, the update unit can acquire environmental data from multiple data sources and reflect it in determining the eco level. The update unit can also integrate information from different data sources and update the eco level criteria. Furthermore, the update unit can combine information from multiple data sources to enrich the method of displaying the eco level. In this way, by integrating information from different data sources, the accuracy and range of the data are improved. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data from different data sources into the generation AI and cause the generation AI to integrate the data.

[0068] The update unit can prioritize updating the data for each region, taking into account the user's geographical location information, when updating. For example, the update unit can prioritize updating the data for each region, taking into account the user's geographical location information, when updating. For example, the update unit can detect the user's current location and prioritize updating the environmental data for that region. The update unit can also prioritize updating the environmental regulation information for each region based on the user's location information. Furthermore, the update unit can prioritize updating the recyclability data for each region, taking into account the user's location information. In this way, the data for each region can be prioritized by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input the user's location information data to the generation AI and cause the generation AI to update the data for each region.

[0069] The update unit can analyze the user's past update history and propose an optimal update method during an update. For example, the update unit can analyze the user's past update history and propose an optimal update method during an update. For example, the update unit can analyze the user's past update history and propose the most frequently used update method. The update unit can also propose an optimal update frequency based on the user's past update history. Furthermore, the update unit can also propose an optimal data source by referring to the user's past update history. In this way, the optimal update method can be proposed by analyzing the user's past update history. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's past update history data into a generation AI and cause the generation AI to propose an optimal update method.

[0070] The update unit can analyze the user's social media activity and update the related data during an update. For example, the update unit can analyze the user's social media activity and update the related data during an update. For example, the update unit can analyze the user's social media posts and update the related environmental data. The update unit can also update the related environmental regulation information based on the user's social media check-in information. Furthermore, the update unit can update the related recyclability data by referring to the activity of the user's friends on social media. In this way, the related data can be updated by analyzing the user's social media activity. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's social media data into the generation AI and cause the generation AI to update the related data.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The eco-analysis tool analyzes a user's purchasing history and can estimate the eco-level of similar products based on the eco-level of products purchased in the past. For example, if the eco-level of products purchased by the user in the past is high, it can estimate a high eco-level for products in the same category. Conversely, if the eco-level of products purchased by the user in the past is low, it can estimate a low eco-level for products in the same category. Furthermore, it can prioritize products with a high eco-level based on the user's purchasing history. In this way, by utilizing the user's purchasing history, it is possible to estimate the eco-level more accurately.

[0073] The eco analysis tool can determine the eco level by taking the user's health condition into consideration. For example, if the user has an allergy, it can rate the eco level of products that do not contain that allergen highly. Also, if the user is on a specific diet, it can rate the eco level of products that are suitable for that diet highly. Furthermore, it can adjust the eco level of products that contain specific ingredients based on the user's health condition. This makes it possible to determine a more personalized eco level by taking the user's health condition into consideration.

[0074] The eco-analysis tool can analyze a user's social media activity and display the eco-level of related products. For example, it can analyze the content of a user's social media posts and display the eco-level of related products. It can also display the eco-level of related products based on the user's social media check-in information. It can also display the eco-level of related products based on the activity of the user's friends on social media. In this way, it is possible to display the eco-level of related products by analyzing a user's social media activity.

[0075] The eco-analysis tool can apply regional eco-level standards by taking into account the user's geographical location information. For example, it can detect the user's current location and apply the eco-level standards for that region. It can also determine the eco-level by taking into account regional environmental regulations based on the user's location information. It can also evaluate regional recyclability by taking into account the user's location information. This allows the eco-level standards for each region to be applied by taking into account the user's geographical location information.

[0076] The eco analysis tool can analyze the user's past eco level judgment history and suggest the optimal eco level judgment method. For example, it can analyze the user's past eco level judgment history and suggest the most frequently used judgment method. It can also suggest the optimal judgment criteria based on the user's past eco level judgment history. Furthermore, it can also suggest the optimal eco level display method by referring to the user's past eco level judgment history. In this way, it is possible to suggest the optimal judgment method by analyzing the user's past eco level judgment history.

[0077] The eco-analysis tool can analyze a user's social media activity and set a relevant eco-level standard. For example, it can analyze the content of a user's social media posts and set a relevant eco-level standard. It can also set a relevant eco-level standard based on the user's social media check-in information. It can also set a relevant eco-level standard based on the activities of the user's friends on social media. In this way, it is possible to set a relevant eco-level standard by analyzing a user's social media activity.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The camera unit takes a photo of the product. For example, the camera unit can take a photo of the product using the camera on a smartphone or tablet. The camera unit can also automatically adjust the angle and lighting of the product to obtain the optimal image. Specifically, it can automatically adjust the angle of the product to take a photo at the most visible position, and detect the amount of ambient light to take a photo at the optimal brightness. It can also adjust the direction of the light to avoid reflections on the product. Step 2: The image recognition unit analyzes the image captured by the camera unit. For example, the image recognition unit can recognize words written on a product from the image and identify the product. Specifically, it can read information such as the product name, ingredients list, and manufacturer. It can also analyze and identify the product's label or package design. It can also read and identify the product's barcode or QR code. Step 3: The determination unit determines the eco-level based on the product identified by the image recognition unit. For example, the determination unit determines the eco-level based on information about the product's manufacturing process, the materials used, and recyclability. Specifically, the eco-level can be determined taking into account the amount of energy consumed and carbon dioxide emitted in the manufacturing process, the types of materials used and their recyclability, the disposal method, and the difficulty of recycling. Step 4: The display unit displays the eco level determined by the determination unit. For example, the display unit can instantly display the determined eco level on the user's smartphone or tablet. Specifically, it can display detailed eco level information graphically, display the eco level history in chronological order, and compare the eco levels of multiple products.

[0080] (Example 2) An eco-analysis tool according to an embodiment of the present invention is a system that photographs a product, determines its eco-level, and displays it. The eco-analysis tool instantly determines the eco-level of a product by photographing the product with a camera using an application installed on a smartphone or tablet. This application is equipped with image recognition technology, which can recognize words written on the product and identify and analyze the product. For example, in the eco-analysis tool, a user photographs a product using the camera on their smartphone or tablet. For example, the eco-analysis tool sends the image captured with the camera to the image recognition technology. The image recognition technology recognizes words written on the product from the image and identifies the product. For example, it can read information such as the product name, ingredient list, and manufacturer. Next, the eco-analysis tool analyzes the product's eco-level based on the product identified using the image recognition technology. The eco-level determination takes into account information such as the product's manufacturing process, materials used, and recyclability. For example, for plastic products, the carbon dioxide emissions during the manufacturing process and the ease of recycling are evaluated. Next, the eco-analysis tool instantly displays the analysis results on the user's smartphone or tablet. This allows the user to select environmentally friendly products based on the displayed eco-level. For example, choosing products with a high eco-level can reduce environmental impact. This makes it easy for everyone to take environmentally friendly actions. For example, because users can instantly check the eco-level when selecting a product, it becomes easier to make environmentally conscious choices in their daily lives. Furthermore, the quick determination of eco-levels eliminates the need for additional shopping effort. Furthermore, the use of image recognition technology enables highly accurate product identification and analysis. This allows users to obtain reliable eco-level information. For example, even if the same product has different manufacturers or ingredients, the differences can be accurately recognized and the eco-level determined. The eco-analysis tool can be used simply by installing it on a smartphone or tablet; no special equipment or settings are required. This makes it easy for a wide range of users to use. Furthermore, application updates ensure that the latest eco-level information is always available.This makes it easy for everyone to take environmentally friendly actions with the Eco Analysis Tool. For example, when selecting a product, users can instantly check the eco level, making it easier to make environmentally conscious choices in their daily lives. Furthermore, the quick determination of eco levels eliminates the need for additional shopping effort. Furthermore, the use of image recognition technology enables highly accurate product identification and analysis. This allows users to obtain highly reliable eco level information. For example, even if the same product has different manufacturers or ingredients, the differences can be accurately recognized and the eco level determined. The Eco Analysis Tool can be used simply by installing it on a smartphone or tablet; no special equipment or settings are required. This makes it easy for a wide range of users to use. Furthermore, by updating the application, the latest eco level information can always be provided.

[0081] The eco-analysis tool according to the embodiment includes a camera unit, an image recognition unit, a determination unit, and a display unit. The camera unit photographs a product. For example, the camera unit photographs the product using a smartphone or tablet camera. The camera unit can also automatically adjust the angle and lighting of the product during photography to obtain the optimal image. For example, the camera unit can automatically adjust the angle of the product to photograph it in the most visible position. The camera unit can also detect the amount of ambient light and photograph it at the optimal brightness. The camera unit can also adjust the direction of the light to avoid reflections on the product during photography. The image recognition unit analyzes the image photographed by the camera unit. For example, the image recognition unit recognizes words written on the product from the image and identifies the product. The image recognition unit can read information such as the product name, ingredient list, and manufacturer. The image recognition unit can also analyze the label or package design of the product to identify the product. For example, the image recognition unit can analyze the text written on the product label to identify the product. The image recognition unit can also analyze the package design to identify the product. Furthermore, the image recognition unit can read and identify the barcode or two-dimensional code (e.g., QR code) of the product. For example, the image recognition unit can read the barcode of the product to identify the product. The image recognition unit can also read the two-dimensional code of the product to identify the product. The determination unit determines the eco level based on the product identified by the image recognition unit. For example, the determination unit determines the eco level based on information about the product's manufacturing process, the materials used, and recyclability. The determination unit can determine the eco level by taking into account, for example, the energy consumption and carbon dioxide emissions in the product's manufacturing process. The determination unit can also determine the eco level by evaluating the type of materials used in the product and its recyclability. Furthermore, the determination unit can determine the eco level by taking into account the product's disposal method and the difficulty of recycling. For example, the determination unit can analyze the product's disposal method, evaluate the product's recyclability, and determine the eco level. The display unit displays the eco level determined by the determination unit.For example, the display unit instantly displays the determined eco level on the user's smartphone or tablet. The display unit can, for example, graphically display detailed information about the eco level. The display unit can also display a chronological history of the eco level. Furthermore, the display unit can display comparative information about the eco levels. For example, the display unit can compare multiple products and display their eco levels. As a result, the eco analysis tool according to the embodiment allows the user to select environmentally friendly products by photographing the products and determining and displaying their eco levels.

[0082] The eco-analysis tool includes a standard setting unit that sets eco-level determination criteria. The standard setting unit sets the eco-level determination criteria. For example, the standard setting unit can set the eco-level determination criteria based on information such as the product's manufacturing process, the materials used, and recyclability. The standard setting unit can also set the eco-level determination criteria based on a user's individual environmental awareness and values. For example, the standard setting unit can analyze the user's environmental awareness and reflect it in the eco-level determination criteria. The standard setting unit can also set the eco-level determination criteria taking the user's values ​​into consideration. Furthermore, the standard setting unit can set different eco-level determination criteria for each product category. For example, the standard setting unit can set different eco-level determination criteria for each product category (food, clothing, electrical appliances, etc.). In this way, setting the eco-level determination criteria improves the accuracy of the determination.

[0083] The eco-analysis tool includes an update unit that updates data. The update unit updates the data. For example, the update unit can automatically acquire the latest environmental data and regulatory information and reflect it in the eco-level determination. The update unit can also update the data by reflecting user feedback. For example, the update unit can analyze user feedback and update the eco-level determination data. The update unit can also integrate information from different data sources to expand the data. For example, the update unit can acquire environmental data from multiple data sources and reflect it in the eco-level determination. In this way, by updating the data, it is possible to always provide the latest eco-level information.

[0084] The image recognition unit can recognize words written on a product and identify the product. The image recognition unit can, for example, recognize words written on a product and identify the product. For example, the image recognition unit can read information such as the product name, ingredient list, and manufacturer. The image recognition unit can also analyze the product's label or package design to identify the product. For example, the image recognition unit can analyze the characters written on the product's label to identify the product. The image recognition unit can also analyze the product's package design to identify the product. The image recognition unit can also read the product's barcode or two-dimensional code to identify the product. For example, the image recognition unit can read the product's barcode to identify the product. The image recognition unit can also read the product's two-dimensional code to identify the product. In this way, the product can be accurately identified by recognizing the words written on the product.

[0085] The determination unit can determine the eco level based on information about the product's manufacturing process, the materials used, and recyclability. The determination unit can determine the eco level based on information about the product's manufacturing process, the materials used, and recyclability. For example, the determination unit can determine the eco level by taking into account the amount of energy consumed and carbon dioxide emissions in the product's manufacturing process. The determination unit can also determine the eco level by evaluating the types of materials used in the product and their recyclability. Furthermore, the determination unit can determine the eco level by taking into account the product's disposal method and the difficulty of recycling. For example, the determination unit can analyze the product's disposal method, evaluate the recyclability, and determine the eco level. In this way, the eco level can be determined with high accuracy by taking into account the product's manufacturing process, materials, and recyclability.

[0086] The display unit can instantly display the determined eco level on the user's smartphone or tablet. For example, the display unit can instantly display the determined eco level on the user's smartphone or tablet. For example, the display unit can graphically display detailed information about the eco level. The display unit can also display the eco level history in chronological order. Furthermore, the display unit can display comparative information about the eco levels. For example, the display unit can compare the eco levels of multiple products and display them. This allows the user to quickly select environmentally friendly products by instantly displaying the eco level.

[0087] The camera unit estimates the user's emotions and adjusts the timing of capturing images based on the estimated user emotions. For example, the camera unit estimates the user's emotions and adjusts the timing of capturing images based on the estimated user emotions. For example, when the user is excited, the camera unit automatically takes continuous images and selects the clearest image. When the user is relaxed, the camera unit can take images at a slower pace to obtain the optimal image. When the user is in a hurry, the camera unit can take images immediately to quickly obtain the image. This allows the optimal image to be obtained by adjusting the timing of capturing images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the camera unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] The camera unit can automatically adjust the angle and lighting of the product when taking a photo to obtain the optimal image. The camera unit can automatically adjust the angle and lighting of the product to obtain the optimal image. For example, the camera unit can automatically adjust the angle of the product to take a photo at the most visible position. The camera unit can also detect the amount of ambient light and take a photo at the optimal brightness. Furthermore, the camera unit can adjust the direction of the light to avoid reflections on the product when taking a photo. In this way, the optimal image can be obtained by automatically adjusting the angle and lighting of the product. Some or all of the above-mentioned processing in the camera unit may be performed using, or without, AI, for example. For example, the camera unit can input image data of the product to a generation AI and cause the generation AI to obtain the optimal image.

[0089] The camera unit can continuously capture multiple images during shooting and select the clearest image. For example, the camera unit can continuously capture multiple images during shooting and select the clearest image. For example, the camera unit can continuously capture five images and select the clearest image from among them. The camera unit can also analyze the continuously captured images and select the image with the least blur. Furthermore, the camera unit can select the image with the most accurate color from the continuously captured images. In this way, by continuously capturing multiple images, the clearest image can be selected. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input continuously captured image data to a generation AI and have the generation AI select the clearest image.

[0090] The camera unit may be equipped with a filtering function that removes background other than the product when capturing an image. The camera unit may be equipped with a filtering function that removes background other than the product when capturing an image. For example, the camera unit may detect background other than the product and automatically remove it. The camera unit may also perform filtering that blurs the background to highlight the product. Furthermore, the camera unit may also trim unnecessary parts other than the product to obtain an optimal image. In this way, an image that highlights the product can be obtained by automatically removing background other than the product. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit may input captured image data to a generation AI and have the generation AI remove the background.

[0091] The camera unit can estimate the user's emotions and determine the priority of products to be photographed based on the estimated user emotions. The camera unit, for example, estimates the user's emotions and determines the priority of products to be photographed based on the estimated user emotions. For example, when the user is excited, the camera unit can prioritize photographing popular products. Furthermore, when the user is relaxed, the camera unit can prioritize photographing products with a high eco-friendliness level. Furthermore, when the user is in a hurry, the camera unit can prioritize photographing products that are close at hand. This allows the user to photograph the best products by determining the priority of products based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0092] The camera unit can prioritize photographing highly relevant products by taking into account the user's geographical location information when taking a photo. For example, the camera unit can prioritize photographing highly relevant products by taking into account the user's geographical location information when taking a photo. For example, the camera unit can detect the user's current location and prioritize photographing products that are popular in the area. The camera unit can also prioritize photographing local specialties based on the user's location information. Furthermore, the camera unit can prioritize photographing products sold in nearby stores by taking into account the user's location information. In this way, highly relevant products can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the camera unit may be performed using, or without, AI. For example, the camera unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant products.

[0093] The camera unit can analyze the user's past shooting history and suggest the optimal shooting method when taking a photo. For example, the camera unit can analyze the user's past shooting history and suggest the optimal shooting method when taking a photo. For example, the camera unit can analyze the user's past shooting history and suggest the most frequently used shooting method. The camera unit can also suggest the optimal shooting angle and lighting conditions based on the user's past shooting history. Furthermore, the camera unit can suggest optimal filtering settings by referring to the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing the user's past shooting history. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's past shooting history data into a generation AI and have the generation AI suggest the optimal shooting method.

[0094] The camera unit can analyze the user's social media activity and capture related products when taking a photo. For example, the camera unit can analyze the user's social media activity and capture related products when taking a photo. For example, the camera unit can analyze the user's social media posts and capture related products. The camera unit can also capture related products based on the user's social media check-in information. Furthermore, the camera unit can also capture related products by referring to the activity of the user's friends on social media. In this way, related products can be captured by analyzing the user's social media activity. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the user's social media data into a generation AI and cause the generation AI to select related products.

[0095] The image recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. The image recognition unit, for example, estimates the user's emotion and adjusts the recognition accuracy based on the estimated user's emotion. For example, the image recognition unit can perform detailed analysis to achieve high-precision recognition when the user is excited. The image recognition unit can also perform analysis with standard recognition accuracy when the user is relaxed. Furthermore, the image recognition unit can perform simplified analysis to achieve quick recognition when the user is in a hurry. By adjusting the recognition accuracy based on the user's emotion, optimal recognition results can be obtained. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image recognition unit can be performed using, for example, an AI. For example, the image recognition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The image recognition unit can analyze and identify the product's label or package design during recognition. The image recognition unit, for example, analyzes and identifies the product's label or package design during recognition. For example, the image recognition unit can analyze the characters written on the product's label to identify the product. The image recognition unit can also analyze the product's package design to identify the product. Furthermore, the image recognition unit can analyze the product's label and package design in combination to identify the product. In this way, the product can be accurately identified by analyzing the product's label or package design. Some or all of the above-mentioned processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input data on the product's label or package design into a generation AI and have the generation AI identify the product.

[0097] The image recognition unit can read and identify the barcode or two-dimensional code of a product during recognition. The image recognition unit, for example, reads and identifies the barcode or two-dimensional code of a product during recognition. For example, the image recognition unit can read the barcode of a product to identify the product. The image recognition unit can also read the two-dimensional code of a product to identify the product. Furthermore, the image recognition unit can read both the barcode and the two-dimensional code to identify the product. This makes it possible to accurately identify the product by reading the barcode or two-dimensional code of the product. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input data of the barcode or two-dimensional code of the product to the generation AI and have the generation AI identify the product.

[0098] The image recognition unit can analyze and identify the shape and color of a product during recognition. The image recognition unit, for example, analyzes and identifies the shape and color of a product during recognition. For example, the image recognition unit can analyze the shape of a product to identify the product. The image recognition unit can also analyze the color of a product to identify the product. Furthermore, the image recognition unit can analyze a combination of the shape and color of a product to identify the product. In this way, by analyzing the shape and color of a product, the product can be accurately identified. Some or all of the above-mentioned processing in the image recognition unit may be performed using, or without, AI, for example. For example, the image recognition unit can input data on the shape and color of a product to a generation AI and cause the generation AI to identify the product.

[0099] The image recognition unit can estimate the user's emotion and adjust the display method of the recognition result based on the estimated user emotion. The image recognition unit, for example, estimates the user's emotion and adjusts the display method of the recognition result based on the estimated user emotion. For example, the image recognition unit can display the recognition result in detail when the user is excited. The image recognition unit can also display the recognition result in a standard manner when the user is relaxed. Furthermore, the image recognition unit can also display the recognition result in a concise manner when the user is in a hurry. This allows the optimal display for the user by adjusting the display method of the recognition result based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The image recognition unit can prioritize recognizing highly relevant products by taking into account the user's geographical location information during recognition. For example, the image recognition unit can prioritize recognizing highly relevant products by taking into account the user's geographical location information during recognition. For example, the image recognition unit can detect the user's current location and prioritize recognizing products that are popular in that area. The image recognition unit can also prioritize recognizing local specialties based on the user's location information. Furthermore, the image recognition unit can prioritize recognizing products sold in nearby stores by taking into account the user's location information. In this way, highly relevant products can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the image recognition unit may be performed using, or without, AI. For example, the image recognition unit can input the user's location information data into the generation AI and cause the generation AI to recognize highly relevant products.

[0101] The image recognition unit can analyze the user's past recognition history during recognition and suggest the optimal recognition method. For example, the image recognition unit can analyze the user's past recognition history and suggest the most frequently used recognition method during recognition. The image recognition unit can also suggest the optimal recognition angle and lighting conditions based on the user's past recognition history. Furthermore, the image recognition unit can also suggest optimal filtering settings by referring to the user's past recognition history. In this way, the optimal recognition method can be suggested by analyzing the user's past recognition history. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input the user's past recognition history data into a generation AI and have the generation AI suggest the optimal recognition method.

[0102] The image recognition unit can analyze the user's social media activity during recognition and recognize related products. For example, the image recognition unit can analyze the user's social media activity during recognition and recognize related products. For example, the image recognition unit can analyze the user's social media posts and recognize related products. The image recognition unit can also recognize related products based on the user's social media check-in information. Furthermore, the image recognition unit can recognize related products by referring to the activity of the user's friends on social media. In this way, related products can be recognized by analyzing the user's social media activity. Some or all of the above-described processing in the image recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the image recognition unit can input the user's social media data into a generation AI and cause the generation AI to recognize related products.

[0103] The determination unit can estimate the user's emotions and adjust the eco-level determination criteria based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the eco-level determination criteria based on the estimated user emotions. For example, the determination unit can set stricter eco-level determination criteria when the user is excited. The determination unit can also set standard eco-level determination criteria when the user is relaxed. Furthermore, the determination unit can also set simplified eco-level determination criteria when the user is in a hurry. This enables a more appropriate eco-level determination by adjusting the eco-level determination criteria based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit can be performed using, for example, an AI. For example, the determination unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] The determination unit can take into account the energy consumption in the manufacturing process of the product when making the determination. The determination unit, for example, takes into account the energy consumption in the manufacturing process of the product when making the determination. For example, the determination unit can analyze the energy consumption used in the manufacturing process of the product and determine the eco level. The determination unit can also determine the eco level by taking into account the carbon dioxide emissions in the manufacturing process of the product. Furthermore, the determination unit can evaluate the energy efficiency in the manufacturing process of the product and determine the eco level. In this way, by taking into account the energy consumption in the manufacturing process of the product, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input product manufacturing process data into the generation AI and cause the generation AI to analyze the energy consumption.

[0105] The determination unit can evaluate the disposal method and recyclability of the product after use at the time of determination. The determination unit, for example, evaluates the disposal method and recyclability of the product after use at the time of determination. For example, the determination unit can analyze the disposal method of the product, evaluate the recyclability, and determine the eco level. The determination unit can also determine the eco level taking into account the difficulty of recycling the product. Furthermore, the determination unit can evaluate the environmental impact of the product after disposal and determine the eco level. In this way, by evaluating the disposal method and recyclability of the product after use, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the disposal method and recyclability of the product into the generation AI and have the generation AI perform the evaluation.

[0106] The determination unit can determine the eco level by taking into account the transport distance and means of transport of the product during the determination. For example, the determination unit can determine the eco level by taking into account the transport distance and means of transport of the product during the determination. For example, the determination unit can analyze the transport distance of the product and determine the eco level. The determination unit can also determine the eco level by taking into account the means of transport of the product (ship, airplane, truck, etc.). Furthermore, the determination unit can evaluate the energy consumption during the transport process of the product and determine the eco level. In this way, by taking into account the transport distance and means of transport of the product, the eco level can be determined with high accuracy. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the transport distance and means of transport of the product into the generation AI and cause the generation AI to perform the evaluation.

[0107] The determination unit can estimate the user's emotion and adjust the display method of the eco level based on the estimated user emotion. The determination unit, for example, estimates the user's emotion and adjusts the display method of the eco level based on the estimated user emotion. For example, the determination unit can display the eco level in detail when the user is excited. The determination unit can also display the eco level in a standard manner when the user is relaxed. Furthermore, the determination unit can also display the eco level in a concise manner when the user is in a hurry. This allows the display method of the eco level to be optimized for the user by adjusting it based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed using an AI, for example, or without an AI. For example, the determination unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0108] The determination unit can apply regional eco-level standards by taking into account the user's geographical location information during the determination. For example, the determination unit can apply regional eco-level standards by taking into account the user's geographical location information during the determination. For example, the determination unit can detect the user's current location and apply the regional eco-level standards. The determination unit can also determine the eco level by taking into account regional environmental regulations based on the user's location information. Furthermore, the determination unit can evaluate regional recyclability by taking into account the user's location information. In this way, regional eco-level standards can be applied by taking into account the user's geographical location information. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's location information data into the generation AI and cause the generation AI to apply regional eco-level standards.

[0109] The judgment unit can analyze the user's past judgment history and propose an optimal judgment method when making a judgment. For example, the judgment unit can analyze the user's past judgment history and propose an optimal judgment method when making a judgment. For example, the judgment unit can analyze the user's past judgment history and propose the most frequently used judgment method. The judgment unit can also propose optimal judgment criteria based on the user's past judgment history. Furthermore, the judgment unit can also propose an optimal eco level display method by referring to the user's past judgment history. In this way, the optimal judgment method can be proposed by analyzing the user's past judgment history. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past judgment history data into a generation AI and cause the generation AI to propose an optimal judgment method.

[0110] The determination unit can analyze the user's social media activity at the time of determination and determine the eco-level of the related products. For example, the determination unit can analyze the user's social media activity at the time of determination and determine the eco-level of the related products. For example, the determination unit can analyze the content of the user's social media posts and determine the eco-level of the related products. The determination unit can also determine the eco-level of the related products based on the user's check-in information on social media. Furthermore, the determination unit can determine the eco-level of the related products by referring to the activity of the user's friends on social media. In this way, the eco-level of the related products can be determined by analyzing the user's social media activity. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media data into a generation AI and cause the generation AI to determine the eco-level of the related products.

[0111] The display unit can estimate the user's emotions and adjust the display format of the eco level based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display format of the eco level based on the estimated user emotions. For example, the display unit can display the eco level in detail when the user is excited. The display unit can also display the eco level in a standard manner when the user is relaxed. Furthermore, the display unit can also display the eco level in a concise manner when the user is in a hurry. This allows the display format of the eco level to be optimized for the user by adjusting it based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0112] The display unit can graphically display the detailed eco level information when displaying the data. The display unit, for example, graphically displays the detailed eco level information when displaying the data. For example, the display unit can display the detailed eco level information in a graph or chart. The display unit can also visually display the detailed eco level information using icons or symbols. Furthermore, the display unit can display the detailed eco level information in an infographic. This makes it easier for the user to visually understand the detailed eco level information by graphically displaying it. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the detailed eco level data to a generation AI and cause the generation AI to execute a graphical display.

[0113] The display unit can display the eco level history in chronological order when displaying. The display unit, for example, displays the eco level history in chronological order when displaying. For example, the display unit can display the eco level history in a chronological graph. The display unit can also display the eco level history in a chronological list. Furthermore, the display unit can display the eco level history in chronological order in a calendar format. By displaying the eco level history in chronological order, the user can understand past fluctuations in the eco level. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input eco level history data to a generation AI and cause the generation AI to display the data in chronological order.

[0114] The display unit can display comparative information on eco levels when displaying. The display unit, for example, displays comparative information on eco levels when displaying. For example, the display unit can compare multiple products and display their eco levels. The display unit can also compare different batches of the same product and display their eco levels. Furthermore, the display unit can compare products with high and low eco levels and display them. This allows the user to compare and select multiple products by displaying comparative information on eco levels. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input comparative information data on eco levels to a generation AI and cause the generation AI to display the comparative information.

[0115] The display unit can estimate the user's emotions and adjust the display order of the eco levels based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display order of the eco levels based on the estimated user emotions. For example, if the user is excited, the display unit can prioritize displaying products with high eco levels. If the user is relaxed, the display unit can also display the eco levels in a standard order. If the user is in a hurry, the display unit can also display the eco levels in a concise order. This allows the display order of the eco levels to be optimized for the user by adjusting the display order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0116] The display unit can display the eco level for each region taking into account the user's geographical location information when displaying the data. The display unit, for example, can display the eco level for each region taking into account the user's geographical location information when displaying the data. For example, the display unit can detect the user's current location and display the eco level for that region. The display unit can also display the eco level for each region based on the user's location information. Furthermore, the display unit can display the recyclability for each region taking into account the user's location information. In this way, the eco level for each region can be displayed by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the user's location information data to a generation AI and cause the generation AI to display the eco level for each region.

[0117] The display unit can analyze the user's past display history and propose an optimal display method when displaying. For example, the display unit can analyze the user's past display history and propose an optimal display method when displaying. For example, the display unit can analyze the user's past display history and propose the most frequently used display method. The display unit can also propose an optimal display order based on the user's past display history. Furthermore, the display unit can also propose an optimal eco level display method by referring to the user's past display history. In this way, the optimal display method can be proposed by analyzing the user's past display history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display history data to a generation AI and cause the generation AI to propose an optimal display method.

[0118] The display unit can analyze the user's social media activity and display the associated eco level when displaying the data. For example, the display unit can analyze the user's social media activity and display the associated eco level when displaying the data. For example, the display unit can analyze the content of the user's social media posts and display the associated eco level. The display unit can also display the associated eco level based on the user's social media check-in information. Furthermore, the display unit can display the associated eco level with reference to the activity of the user's friends on social media. In this way, the associated eco level can be displayed by analyzing the user's social media activity. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and cause the generation AI to display the associated eco level.

[0119] The standard setting unit can estimate the user's emotions and adjust the eco-level standard based on the estimated user's emotions. The standard setting unit, for example, estimates the user's emotions and adjusts the eco-level standard based on the estimated user's emotions. For example, the standard setting unit can set a strict eco-level standard when the user is excited. The standard setting unit can also set a standard eco-level standard when the user is relaxed. Furthermore, the standard setting unit can also set a simplified eco-level standard when the user is in a hurry. This allows for more appropriate standard setting by adjusting the eco-level standard based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the standard setting unit can be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0120] The standard setting unit can reflect the latest environmental regulations and standards when setting the standards. The standard setting unit, for example, reflects the latest environmental regulations and standards when setting the standards. For example, the standard setting unit can automatically obtain the latest environmental regulations and reflect them in the eco-level standards. The standard setting unit can also analyze the latest environmental standards and reflect them in the eco-level standards. Furthermore, the standard setting unit can update the eco-level standards based on the latest environmental data. In this way, by reflecting the latest environmental regulations and standards, the eco-level can always be determined based on the latest standards. Some or all of the above-mentioned processing in the standard setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the standard setting unit can input data on the latest environmental regulations and standards into the generation AI and cause the generation AI to update the standards.

[0121] The standard setting unit can reflect the user's individual environmental awareness and values ​​when setting the standard. For example, the standard setting unit can analyze the user's environmental awareness and reflect it in the eco-level standard. The standard setting unit can also set the eco-level standard taking the user's values ​​into consideration. Furthermore, the standard setting unit can set the eco-level standard by combining the user's environmental awareness and values. This allows the user's individual environmental awareness and values ​​to be reflected, thereby enabling the optimal standard to be set for the user. Some or all of the above-mentioned processing in the standard setting unit can be performed using, or without, AI. For example, the standard setting unit can input data on the user's environmental awareness and values ​​into the generation AI and have the generation AI set the standard.

[0122] The standard setting unit can apply different standards to each product category when setting the standards. For example, the standard setting unit can set different eco-level standards for each product category (food, clothing, electrical appliances, etc.). The standard setting unit can also analyze the environmental impact of each product category and set eco-level standards. Furthermore, the standard setting unit can set eco-level standards taking into account the recyclability of each product category. This allows for more accurate determination of eco-levels by applying different standards to each product category. Some or all of the above-mentioned processing in the standard setting unit can be performed using, for example, AI, or can be performed without using AI. For example, the standard setting unit can input data for each product category into a generation AI and have the generation AI set the standards.

[0123] The standard setting unit can estimate the user's emotions and determine the priority of standard setting based on the estimated user emotions. The standard setting unit, for example, estimates the user's emotions and determines the priority of standard setting based on the estimated user emotions. For example, the standard setting unit can prioritize the eco level standard setting when the user is excited. The standard setting unit can also set the eco level standard as a standard when the user is relaxed. Furthermore, the standard setting unit can quickly set the eco level standard when the user is in a hurry. This enables optimal standard setting for the user by determining the priority of standard setting based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the standard setting unit may be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0124] The standard setting unit can apply regional standards by taking into account the user's geographical location information when setting the standards. For example, the standard setting unit can apply regional standards by taking into account the user's geographical location information when setting the standards. For example, the standard setting unit can detect the user's current location and apply the eco-level standard for that region. The standard setting unit can also set eco-level standards by taking into account regional environmental regulations based on the user's location information. Furthermore, the standard setting unit can evaluate the recyclability of each region by taking into account the user's location information. In this way, regional standards can be applied by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the standard setting unit can be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's location information data into the generation AI and cause the generation AI to apply regional standards.

[0125] The standard setting unit can analyze the user's past standard setting history and propose optimal standards when setting standards. For example, the standard setting unit can analyze the user's past standard setting history and propose optimal standards when setting standards. For example, the standard setting unit can analyze the user's past standard setting history and propose the most frequently used standard. The standard setting unit can also propose optimal standards based on the user's past standard setting history. Furthermore, the standard setting unit can propose optimal eco level standards by referring to the user's past standard setting history. In this way, optimal standards can be proposed by analyzing the user's past standard setting history. Some or all of the above-mentioned processing in the standard setting unit can be performed, for example, using AI or without AI. For example, the standard setting unit can input the user's past standard setting history data into a generation AI and cause the generation AI to propose optimal standards.

[0126] The standard setting unit may analyze the user's social media activity and set the relevant standard when setting the standard. For example, the standard setting unit may analyze the user's social media activity and set the relevant standard when setting the standard. For example, the standard setting unit may analyze the user's social media posts and set the relevant eco-level standard. The standard setting unit may also set the relevant eco-level standard based on the user's social media check-in information. Furthermore, the standard setting unit may set the relevant eco-level standard with reference to the activities of the user's friends on social media. In this way, the relevant standard can be set by analyzing the user's social media activity. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the standard setting unit may input the user's social media data into a generation AI and cause the generation AI to set the relevant standard.

[0127] The update unit can estimate the user's emotion and adjust the data update frequency based on the estimated user's emotion. The update unit, for example, estimates the user's emotion and adjusts the data update frequency based on the estimated user's emotion. For example, the update unit can set the data update frequency high when the user is excited. The update unit can also set the data update frequency to standard when the user is relaxed. Furthermore, the update unit can also set the data update frequency low when the user is in a hurry. By adjusting the data update frequency based on the user's emotion, updates that are optimal for the user are possible. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the update unit may be performed using an AI, or may be performed without an AI. For example, the update unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0128] The update unit can automatically acquire the latest environmental data and regulatory information during an update. The update unit automatically acquires the latest environmental data and regulatory information during an update, for example. For example, the update unit can automatically acquire the latest environmental data and reflect it in determining the eco level. The update unit can also automatically acquire the latest environmental regulatory information and reflect it in the eco level criteria. Furthermore, the update unit can acquire a combination of the latest environmental data and regulatory information and reflect it in determining the eco level. In this way, by automatically acquiring the latest environmental data and regulatory information, the eco level can always be determined using the latest information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input the latest environmental data and regulatory information data into the generation AI and cause the generation AI to acquire the data.

[0129] The update unit can update the data by reflecting user feedback during an update. The update unit, for example, updates the data by reflecting user feedback during an update. For example, the update unit can analyze user feedback and update the eco level determination data. The update unit can also adjust the eco level criteria based on user feedback. Furthermore, the update unit can update the eco level display method by reflecting user feedback. This enables data updating that is more suitable for the user by reflecting user feedback. Some or all of the above-described processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input user feedback data to a generation AI and cause the generation AI to update the data.

[0130] The update unit can integrate information from different data sources to enrich the data during an update. For example, the update unit can integrate information from different data sources to enrich the data during an update. For example, the update unit can acquire environmental data from multiple data sources and reflect it in determining the eco level. The update unit can also integrate information from different data sources and update the eco level criteria. Furthermore, the update unit can combine information from multiple data sources to enrich the method of displaying the eco level. In this way, by integrating information from different data sources, the accuracy and range of the data are improved. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data from different data sources into the generation AI and cause the generation AI to integrate the data.

[0131] The update unit can estimate the user's emotions and determine the priority of update data based on the estimated user emotions. The update unit, for example, estimates the user's emotions and determines the priority of update data based on the estimated user emotions. For example, if the user is excited, the update unit can prioritize updating important data. Also, if the user is relaxed, the update unit can update standard data. Furthermore, if the user is in a hurry, the update unit can quickly update data. This enables optimal data updating for the user by determining the priority of update data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using an AI, for example, or without an AI. For example, the update unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0132] The update unit can prioritize updating the data for each region, taking into account the user's geographical location information, when updating. For example, the update unit can prioritize updating the data for each region, taking into account the user's geographical location information, when updating. For example, the update unit can detect the user's current location and prioritize updating the environmental data for that region. The update unit can also prioritize updating the environmental regulation information for each region based on the user's location information. Furthermore, the update unit can prioritize updating the recyclability data for each region, taking into account the user's location information. In this way, the data for each region can be prioritized by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input the user's location information data to the generation AI and cause the generation AI to update the data for each region.

[0133] The update unit can analyze the user's past update history and propose an optimal update method during an update. For example, the update unit can analyze the user's past update history and propose an optimal update method during an update. For example, the update unit can analyze the user's past update history and propose the most frequently used update method. The update unit can also propose an optimal update frequency based on the user's past update history. Furthermore, the update unit can also propose an optimal data source by referring to the user's past update history. In this way, the optimal update method can be proposed by analyzing the user's past update history. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's past update history data into a generation AI and cause the generation AI to propose an optimal update method.

[0134] The update unit can analyze the user's social media activity and update the related data during an update. For example, the update unit can analyze the user's social media activity and update the related data during an update. For example, the update unit can analyze the user's social media posts and update the related environmental data. The update unit can also update the related environmental regulation information based on the user's social media check-in information. Furthermore, the update unit can update the related recyclability data by referring to the activity of the user's friends on social media. In this way, the related data can be updated by analyzing the user's social media activity. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's social media data into the generation AI and cause the generation AI to update the related data. === Hard Collateral 1-1 === Each of the multiple elements, including the camera unit, image recognition unit, determination unit, display unit, standard setting unit, and update unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the camera unit takes a picture of a product using the camera 42 of the smart device 14. The image recognition unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and analyzes the captured image. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the eco level of the product. The display unit displays the eco level using the display 40A of the smart device 14. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets criteria for determining the eco level. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates data. === Hard Collateral 1-2 === Each of the multiple elements, including the camera unit, image recognition unit, determination unit, display unit, standard setting unit, and update unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the camera unit photographs a product using the camera 42 of the smart glasses 214. The image recognition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and analyzes the captured image. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the eco level of the product. The display unit displays the eco level using the display of the smart glasses 214. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets criteria for determining the eco level. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates data. === Hard Collateral 1-3 === Each of the multiple elements including the camera unit, image recognition unit, determination unit, display unit, standard setting unit, and update unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the camera unit takes a picture of a product using the camera 42 of the headset type terminal 314. The image recognition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and analyzes the captured image. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the eco level of the product. The display unit displays the eco level using the display 343 of the headset type terminal 314. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets criteria for determining the eco level. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates data. === Hard Collateral 1-4 === Each of the multiple elements including the camera unit, image recognition unit, determination unit, display unit, standard setting unit, and update unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the camera unit takes a picture of a product using the camera 42 of the robot 414. The image recognition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and analyzes the captured image. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the eco level of the product. The display unit displays the eco level using the display of the robot 414. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets criteria for determining the eco level. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the data.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The eco-analysis tool analyzes a user's purchasing history and can estimate the eco-level of similar products based on the eco-level of products purchased in the past. For example, if the eco-level of products purchased by the user in the past is high, it can estimate a high eco-level for products in the same category. Conversely, if the eco-level of products purchased by the user in the past is low, it can estimate a low eco-level for products in the same category. Furthermore, it can prioritize products with a high eco-level based on the user's purchasing history. In this way, by utilizing the user's purchasing history, it is possible to estimate the eco-level more accurately.

[0137] The eco analysis tool can determine the eco level by taking the user's health condition into consideration. For example, if the user has an allergy, it can rate the eco level of products that do not contain that allergen highly. Also, if the user is on a specific diet, it can rate the eco level of products that are suitable for that diet highly. Furthermore, it can adjust the eco level of products that contain specific ingredients based on the user's health condition. This makes it possible to determine a more personalized eco level by taking the user's health condition into consideration.

[0138] The eco analysis tool can estimate the user's emotions and adjust the display method of the eco level based on the estimated emotions. For example, if the user is excited, the eco level can be displayed in detail. If the user is relaxed, the eco level can be displayed in a standard manner. Furthermore, if the user is in a hurry, the eco level can be displayed in a concise manner. In this way, by adjusting the display method of the eco level based on the user's emotions, it is possible to provide an optimal display for the user.

[0139] The eco-analysis tool can analyze a user's social media activity and display the eco-level of related products. For example, it can analyze the content of a user's social media posts and display the eco-level of related products. It can also display the eco-level of related products based on the user's social media check-in information. It can also display the eco-level of related products based on the activity of the user's friends on social media. In this way, it is possible to display the eco-level of related products by analyzing a user's social media activity.

[0140] The eco analysis tool can estimate the user's emotions and adjust the eco level determination criteria based on the estimated emotions. For example, if the user is excited, the eco level determination criteria can be set to be strict. If the user is relaxed, the eco level determination criteria can be set to be standard. Furthermore, if the user is in a hurry, the eco level determination criteria can be set to be simplified. In this way, by adjusting the eco level determination criteria based on the user's emotions, more appropriate eco level determination can be achieved.

[0141] The eco-analysis tool can apply regional eco-level standards by taking into account the user's geographical location information. For example, it can detect the user's current location and apply the eco-level standards for that region. It can also determine the eco-level by taking into account regional environmental regulations based on the user's location information. It can also evaluate regional recyclability by taking into account the user's location information. This allows the eco-level standards for each region to be applied by taking into account the user's geographical location information.

[0142] The eco analysis tool can estimate the user's emotions and adjust the display order of eco levels based on the estimated emotions. For example, if the user is excited, products with high eco levels can be displayed preferentially. If the user is relaxed, eco levels can be displayed in a standard order. Furthermore, if the user is in a hurry, eco levels can be displayed in a concise order. In this way, by adjusting the display order of eco levels based on the user's emotions, it is possible to display products in the order that is optimal for the user.

[0143] The eco analysis tool can analyze the user's past eco level judgment history and suggest the optimal eco level judgment method. For example, it can analyze the user's past eco level judgment history and suggest the most frequently used judgment method. It can also suggest the optimal judgment criteria based on the user's past eco level judgment history. Furthermore, it can also suggest the optimal eco level display method by referring to the user's past eco level judgment history. In this way, it is possible to suggest the optimal judgment method by analyzing the user's past eco level judgment history.

[0144] The eco analysis tool can estimate the user's emotions and adjust the eco level standard based on the estimated emotions. For example, if the user is excited, the eco level standard can be set to a stricter value. If the user is relaxed, the eco level standard can be set to a standard value. Furthermore, if the user is in a hurry, the eco level standard can be set to a simplified value. This allows for more appropriate standard setting by adjusting the eco level standard based on the user's emotions.

[0145] The eco-analysis tool can analyze a user's social media activity and set a relevant eco-level standard. For example, it can analyze the content of a user's social media posts and set a relevant eco-level standard. It can also set a relevant eco-level standard based on the user's social media check-in information. It can also set a relevant eco-level standard based on the activities of the user's friends on social media. In this way, it is possible to set a relevant eco-level standard by analyzing a user's social media activity.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The camera unit takes a photo of the product. For example, the camera unit can take a photo of the product using the camera on a smartphone or tablet. The camera unit can also automatically adjust the angle and lighting of the product to obtain the optimal image. Specifically, it can automatically adjust the angle of the product to take a photo at the most visible position, and detect the amount of ambient light to take a photo at the optimal brightness. It can also adjust the direction of the light to avoid reflections on the product. Step 2: The image recognition unit analyzes the image captured by the camera unit. For example, the image recognition unit can recognize words written on a product from the image and identify the product. Specifically, it can read information such as the product name, ingredients list, and manufacturer. It can also analyze and identify the product's label or package design. It can also read and identify the product's barcode or QR code. Step 3: The determination unit determines the eco-level based on the product identified by the image recognition unit. For example, the determination unit determines the eco-level based on information about the product's manufacturing process, the materials used, and recyclability. Specifically, the eco-level can be determined taking into account the amount of energy consumed and carbon dioxide emitted in the manufacturing process, the types of materials used and their recyclability, the disposal method, and the difficulty of recycling. Step 4: The display unit displays the eco level determined by the determination unit. For example, the display unit can instantly display the determined eco level on the user's smartphone or tablet. Specifically, it can display detailed eco level information graphically, display the eco level history in chronological order, and compare the eco levels of multiple products.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0186] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0187] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0188] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0191] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0192] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0197] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0198] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0202] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0203] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0204] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0205] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0206] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0207] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0208] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0209] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0210] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0211] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0212] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0213] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0214] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0215] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0216] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0218] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0219] [Explanation of symbols]

[0220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A camera unit that takes photos of products, an image recognition unit that analyzes the image captured by the camera unit; a determination unit that determines an eco level based on the product identified by the image recognition unit; a display unit that displays the eco level determined by the determination unit; Equipped with A system characterized by:

2. Equipped with a standard setting unit that sets the eco level judgment criteria 2. The system of claim 1.

3. Equipped with an update unit that updates data 2. The system of claim 1.

4. The image recognition unit Recognizes words written on products and identifies them 2. The system of claim 1.

5. The determination unit Determine the eco-level based on information about the product's manufacturing process, materials used, and recyclability 2. The system of claim 1.

6. The display unit The determined eco level is instantly displayed on the user's smartphone or tablet.

2. The system of claim 1.

7. The camera unit Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The camera unit When taking a photo, the angle and lighting of the product are automatically adjusted to obtain the best possible image.

2. The system of claim 1.

Images